
'''simple docstring'''

import tempfile
import unittest

import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax


if is_flax_available():
     import os

     from flax.core.frozen_dict import unfreeze
     from flax.traverse_util import flatten_dict

     from transformers import FlaxBertModel

     UpperCamelCase_        =							"""0.12"""  # assumed parallelism: 8


@require_flax
@is_staging_test
class 	a_	(unittest.TestCase     ):




       @classmethod
       def 				__UpperCamelCase							(  cls	):
         _lowerCAmelCase							:	Optional[int] 					=       TOKEN
         HfFolder.save_token(_a	)




       @classmethod
       def 				__UpperCamelCase							(  cls	):
         try:
           delete_repo(token=cls._token , repo_id="""test-model-flax"""	)
         except HTTPError:
           pass

         try:
           delete_repo(token=cls._token , repo_id="""valid_org/test-model-flax-org"""	)
         except HTTPError:
           pass




       def 				__UpperCamelCase							(  self	):
         _lowerCAmelCase							:	Any 					=       BertConfig(
             vocab_size=9_9 , hidden_size=3_2 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=3_7	)
         _lowerCAmelCase							:	Tuple 					=       FlaxBertModel(_a	)
         model.push_to_hub("""test-model-flax""" , use_auth_token=self._token	)

         _lowerCAmelCase							:	Dict 					=       FlaxBertModel.from_pretrained(f'{USER}/test-model-flax'	)

         _lowerCAmelCase							:	List[Any] 					=       flatten_dict(unfreeze(model.params	)	)
         _lowerCAmelCase							:	int 					=       flatten_dict(unfreeze(new_model.params	)	)

         for key in base_params.keys():
           _lowerCAmelCase							:	Any 					=       (base_params[key] - new_params[key]).sum().item()
           self.assertLessEqual(_a , 1E-3 , msg=f'{key} not identical'	)

         # Reset repo
         delete_repo(token=self._token , repo_id="""test-model-flax"""	)

         # Push to hub via save_pretrained
         with tempfile.TemporaryDirectory() as tmp_dir:
           model.save_pretrained(_a , repo_id="""test-model-flax""" , push_to_hub=_a , use_auth_token=self._token	)

         _lowerCAmelCase							:	Optional[int] 					=       FlaxBertModel.from_pretrained(f'{USER}/test-model-flax'	)

         _lowerCAmelCase							:	List[str] 					=       flatten_dict(unfreeze(model.params	)	)
         _lowerCAmelCase							:	str 					=       flatten_dict(unfreeze(new_model.params	)	)

         for key in base_params.keys():
           _lowerCAmelCase							:	Union[str, Any] 					=       (base_params[key] - new_params[key]).sum().item()
           self.assertLessEqual(_a , 1E-3 , msg=f'{key} not identical'	)







       def 				__UpperCamelCase							(  self	):
         _lowerCAmelCase							:	Union[str, Any] 					=       BertConfig(
             vocab_size=9_9 , hidden_size=3_2 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=3_7	)
         _lowerCAmelCase							:	Tuple 					=       FlaxBertModel(_a	)
         model.push_to_hub("""valid_org/test-model-flax-org""" , use_auth_token=self._token	)

         _lowerCAmelCase							:	List[str] 					=       FlaxBertModel.from_pretrained("""valid_org/test-model-flax-org"""	)

         _lowerCAmelCase							:	List[str] 					=       flatten_dict(unfreeze(model.params	)	)
         _lowerCAmelCase							:	Optional[Any] 					=       flatten_dict(unfreeze(new_model.params	)	)

         for key in base_params.keys():
           _lowerCAmelCase							:	List[str] 					=       (base_params[key] - new_params[key]).sum().item()
           self.assertLessEqual(_a , 1E-3 , msg=f'{key} not identical'	)

         # Reset repo
         delete_repo(token=self._token , repo_id="""valid_org/test-model-flax-org"""	)

         # Push to hub via save_pretrained
         with tempfile.TemporaryDirectory() as tmp_dir:
           model.save_pretrained(
               _a , repo_id="""valid_org/test-model-flax-org""" , push_to_hub=_a , use_auth_token=self._token	)

         _lowerCAmelCase							:	int 					=       FlaxBertModel.from_pretrained("""valid_org/test-model-flax-org"""	)

         _lowerCAmelCase							:	List[str] 					=       flatten_dict(unfreeze(model.params	)	)
         _lowerCAmelCase							:	Optional[Any] 					=       flatten_dict(unfreeze(new_model.params	)	)

         for key in base_params.keys():
           _lowerCAmelCase							:	Tuple 					=       (base_params[key] - new_params[key]).sum().item()
           self.assertLessEqual(_a , 1E-3 , msg=f'{key} not identical'	)



def 						_UpperCAmelCase					(	_lowerCamelCase    :							Tuple	,   _lowerCamelCase    :							List[str]		)     ->    int:
  _lowerCAmelCase							:	int 					=       True
  _lowerCAmelCase							:	Union[str, Any] 					=       flatten_dict(modela.params		)
  _lowerCAmelCase							:	Optional[Any] 					=       flatten_dict(modela.params		)
  for key in flat_params_a.keys():
    if np.sum(np.abs(flat_params_a[key] - flat_params_a[key]		)		) > 1e-4:
      _lowerCAmelCase							:	str 					=       False

  return models_are_equal



@require_flax
class 	a_	(unittest.TestCase     ):




       def 				__UpperCamelCase							(  self	):
         _lowerCAmelCase							:	Union[str, Any] 					=       BertConfig.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""	)
         _lowerCAmelCase							:	Tuple 					=       FlaxBertModel(_a	)

         _lowerCAmelCase							:	List[str] 					=       """bert"""
         with tempfile.TemporaryDirectory() as tmp_dir:
           model.save_pretrained(os.path.join(_a , _a	)	)

           with self.assertRaises(_a	):
             _lowerCAmelCase							:	Optional[Any] 					=       FlaxBertModel.from_pretrained(_a	)

           _lowerCAmelCase							:	Optional[int] 					=       FlaxBertModel.from_pretrained(_a , subfolder=_a	)

         self.assertTrue(check_models_equal(_a , _a	)	)




       def 				__UpperCamelCase							(  self	):
         _lowerCAmelCase							:	Optional[Any] 					=       BertConfig.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""	)
         _lowerCAmelCase							:	List[Any] 					=       FlaxBertModel(_a	)

         _lowerCAmelCase							:	Union[str, Any] 					=       """bert"""
         with tempfile.TemporaryDirectory() as tmp_dir:
           model.save_pretrained(os.path.join(_a , _a	) , max_shard_size="""10KB"""	)

           with self.assertRaises(_a	):
             _lowerCAmelCase							:	Union[str, Any] 					=       FlaxBertModel.from_pretrained(_a	)

           _lowerCAmelCase							:	Dict 					=       FlaxBertModel.from_pretrained(_a , subfolder=_a	)

         self.assertTrue(check_models_equal(_a , _a	)	)




       def 				__UpperCamelCase							(  self	):
         _lowerCAmelCase							:	Any 					=       """bert"""
         _lowerCAmelCase							:	List[str] 					=       """hf-internal-testing/tiny-random-bert-subfolder"""

         with self.assertRaises(_a	):
           _lowerCAmelCase							:	List[Any] 					=       FlaxBertModel.from_pretrained(_a	)

         _lowerCAmelCase							:	Any 					=       FlaxBertModel.from_pretrained(_a , subfolder=_a	)

         self.assertIsNotNone(_a	)







       def 				__UpperCamelCase							(  self	):
         _lowerCAmelCase							:	str 					=       """bert"""
         _lowerCAmelCase							:	int 					=       """hf-internal-testing/tiny-random-bert-sharded-subfolder"""
         with self.assertRaises(_a	):
           _lowerCAmelCase							:	Union[str, Any] 					=       FlaxBertModel.from_pretrained(_a	)

         _lowerCAmelCase							:	Optional[int] 					=       FlaxBertModel.from_pretrained(_a , subfolder=_a	)

         self.assertIsNotNone(_a	)








"""simple docstring"""




from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map



def        a__					(							snake_case__							)		->   Dict[str, torch.Tensor]:
			lowerCamelCase      =   []
			lowerCamelCase      =   []
			lowerCamelCase      =   []

			for rt in rc.restypes:
						lowerCamelCase      =   rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
						restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]							)
						lowerCamelCase      =   {name: i for i, name in enumerate(snake_case__							)}
						restype_atomaa_to_atomaa_list.append(
						    [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]							)

						restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]							)

			# Add dummy mapping for restype 'UNK'
			restype_atomaa_to_atomaa_list.append([0] * 14							)
			restype_atomaa_to_atomaa_list.append([0] * 37							)
			restype_atomaa_mask_list.append([0.0] * 14							)

			lowerCamelCase      =   torch.tensor(
			    snake_case__			,					dtype=torch.intaa			,					device=protein["""aatype"""].device			,					)
			lowerCamelCase      =   torch.tensor(
			    snake_case__			,					dtype=torch.intaa			,					device=protein["""aatype"""].device			,					)
			lowerCamelCase      =   torch.tensor(
			    snake_case__			,					dtype=torch.floataa			,					device=protein["""aatype"""].device			,					)
			lowerCamelCase      =   protein["""aatype"""].to(torch.long							)

			# create the mapping for (residx, atom14) --> atom37, i.e. an array
			# with shape (num_res, 14) containing the atom37 indices for this protein
			lowerCamelCase      =   restype_atomaa_to_atomaa[protein_aatype]
			lowerCamelCase      =   restype_atomaa_mask[protein_aatype]

			lowerCamelCase      =   residx_atomaa_mask
			lowerCamelCase      =   residx_atomaa_to_atomaa.long()

			# create the gather indices for mapping back
			lowerCamelCase      =   restype_atomaa_to_atomaa[protein_aatype]
			lowerCamelCase      =   residx_atomaa_to_atomaa.long()

			# create the corresponding mask
			lowerCamelCase      =   torch.zeros([21, 37]			,					dtype=torch.floataa			,					device=protein["""aatype"""].device							)
			for restype, restype_letter in enumerate(rc.restypes							):
						lowerCamelCase      =   rc.restype_atoa[restype_letter]
						lowerCamelCase      =   rc.residue_atoms[restype_name]
						for atom_name in atom_names:
									lowerCamelCase      =   rc.atom_order[atom_name]
									lowerCamelCase      =   1

			lowerCamelCase      =   restype_atomaa_mask[protein_aatype]
			lowerCamelCase      =   residx_atomaa_mask

			return protein



def        a__					(							snake_case__							)		->   Dict[str, np.ndarray]:
			lowerCamelCase      =   tree_map(lambda snake_case__							: torch.tensor(snake_case__			,					device=batch["""aatype"""].device							)			,					snake_case__			,					np.ndarray							)
			lowerCamelCase      =   tensor_tree_map(lambda snake_case__							: np.array(snake_case__							)			,					make_atomaa_masks(snake_case__							)							)
			return out



